User-Approved Data Search System for Targeted Advertising
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Solution Overview
Problem
Current marketplaces lack perfect information, leading to inefficiencies in data sharing and search processes, where advertisers and vendors are often anonymous, resulting in wasted resources and ineffective advertising due to a lack of user-specific data.
Innovation Solution
A system and method that allows users to control and share approved personal data to generate targeted search recommendations, using a digital credit system to ensure accurate compensation and prevent gamification, while maintaining user privacy through anonymization and feedback loops.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users share personal data with vendors, then advertising targeting accuracy is improved, but user privacy protection is compromised
Solution Approach 1:
The patent introduces a search engine as an intermediary between users and vendors. The search engine receives user search queries, processes them through a machine learning model that considers user profile data, and returns search results. This intermediary structure allows vendors to access user data indirectly through the search engine's processing, rather than direct access, thus maintaining privacy protection while enabling accurate targeting.
Solution Approach 2:
The patent extracts personal identifiable information (PII) from user profiles before using it for advertising targeting. The machine learning model processes user data to generate targeting signals without exposing actual PII to vendors. This extraction of identifying information while retaining behavioral patterns enables accurate advertising without compromising user privacy.
2Productivity
If vendors access user data directly, then product matching efficiency is improved, but system complexity increases
Solution Approach 1:
The patent implements a self-service mechanism where the search engine automatically processes user queries and generates search results without requiring direct vendor-user data exchange. The machine learning model within the search engine performs the matching function autonomously by processing user search queries against vendor product databases, eliminating the need for complex direct data access systems between vendors and users.
3Measurement precision
If users perform detailed searches, then product finding accuracy is improved, but time and resources consumed increase
Solution Approach 1:
The patent performs preliminary processing of user search queries through a machine learning model that quickly generates relevant search results based on user profiles and search history. This preliminary action occurs automatically when users submit search queries, providing accurate results without requiring users to perform multiple iterative searches or manually filter through大量 information, thus reducing time and resource consumption while maintaining accuracy.
Data Source
AI summary
A system and a method for generating search results based on access to user-approved information enable users to get personalized relevant consumable offers by sharing user-approved user information. The user account is prompted to enter a search query. If the search query is entered, the search query is relayed to the remote server. If the public user information meets the minimum information-sharing requirement of the compatible account, an account from the vendor accounts is designated as compatible account. The search query is compared to each consumable-related entry of the compatible account to identify a matching entry from the consumable-related entries of the compatible account. An offer result from the matching entry of the compatible account is generated. The user account is prompted to view the offer result. A viewing payment is executed between the user account and the compatible account, if the offer result is viewed by the user account.